This article proposes a novel advanced differential evolution method which combines the differential evolution with the modified back-propagation algorithm. This new proposed approach is applied to train an adaptive enhanced neural model for approximating the inverse model of the industrial robot arm. Experimental results demonstrate that the proposed modeling procedure using the new identification approach obtains better convergence and more precision than the traditional back-propagation method or the lonely differential evolution approach. Furthermore, the inverse model of the industrial robot arm using the adaptive enhanced neural model performs outstanding results. KeywordsAdvanced differential evolution, adaptive neural networks model, robot arm, modified back-propagation algorithm, nonlinear system identification, autoregressive with exogenous input model Date

Abstract-In this paper, an adaptive MIMO neural network model is used for simultaneously modeling and identifying the forward kinematics of a 3-DOF robot manipulator. The nonlinear features of the robot manipulator kinematics system are modeled by an adaptive MIMO neural network model based on differential evolution algorithm. A differential evolution algorithm is used to optimally generate the appropriate neural weights so as to perfectly characterize the nonlinear features of the forward kinematics of a 3-DOF robot manipulator. This paper supports the performance of the proposed differential evolution algorithm in comparison with the conventional back-propagation algorithm. The results show that the proposed adaptive MIMO neural network model trained by the differential evolution algorithm for identifying the forward kinematics of a 3-DOF robot manipulator is successfully modeled and performed well.

In this paper, the application of modified genetic algorithms (MGA) in the optimization of the ARX Modelbased observer of the Pneumatic Artificial Muscle (PAM) manipulator is investigated. The new MGA algorithm is proposed from the genetic algorithm with important additional strategies, and consequently yields a faster convergence and a more accurate search. Firstly, MGA-based identification method is used to identify the parameters of the nonlinear PAM manipulator described by an ARX model in the presence of white noise and this result will be validated by MGA and compared with the simple genetic algorithm (GA) and LMS (Least mean-squares) method. Secondly, the intrinsic features of the hysteresis as well as other nonlinear disturbances existing intuitively in the PAM system are estimated online by a Modified Recursive Least Square (MRLS) method in identification experiment. Finally, a highly efficient self-tuning control algorithm Minimum Variance Control (MVC) is taken for tracking the joint angle position trajectory of this PAM manipulator. Experiment results are included to demonstrate the excellent performance of the MGA algorithm in the NARX model-based MVC control system of the PAM system. These results can be applied to model, identify and control other highly nonlinear systems as well.

This paper presents the design, development and implementation of a novel adaptive neural PID (AN-PID) controller suitable for real-time robust walking biped robot control application. The unique feature of the proposed AN-PID controller is that it has highly simple and dynamic selforganizing structure, fast online-tuning speed and flexibility in online-updating. The proposed adaptive algorithm focuses on efficiently optimizing Gain Scheduling and PID weighting parameters of neural MLPNN model integrated in the proposed AN-PID controller. This implemented AN-PID controller aims to successfully control the robust walking of the highly nonlinear full-sized biped robot HUBOT-3. The performance of this novel neural-based PID controller was found to be outperforming in comparison with conventional PID controller.